Propagation Modeling and Defending of a Mobile Sensor Worm in Wireless Sensor and Actuator Networks.

WSANs (Wireless Sensor and Actuator Networks) are derived from traditional wireless sensor networks by introducing mobile actuator elements. Previous studies indicated that mobile actuators can improve network performance in terms of data collection, energy supplementation, etc. However, according to our experimental simulations, the actuator's mobility also causes the sensor worm to spread faster if an attacker launches worm attacks on an actuator and compromises it successfully. Traditional worm propagation models and defense strategies did not consider the diffusion with a mobile worm carrier. To address this new problem, we first propose a microscopic mathematical model to describe the propagation dynamics of the sensor worm. Then, a two-step local defending strategy (LDS) with a mobile patcher (a mobile element which can distribute patches) is designed to recover the network. In LDS, all recovering operations are only taken in a restricted region to minimize the cost. Extensive experimental results demonstrate that our model estimations are rather accurate and consistent with the actual spreading scenario of the mobile sensor worm. Moreover, on average, the LDS outperforms other algorithms by approximately 50% in terms of the cost.

Differential evolution enhanced with multiobjective sorting-based mutation operators.

Differential evolution (DE) is a simple and powerful population-based evolutionary algorithm. The salient feature of DE lies in its mutation mechanism. Generally, the parents in the mutation operator of DE are randomly selected from the population. Hence, all vectors are equally likely to be selected as parents without selective pressure at all. Additionally, the diversity information is always ignored. In order to fully exploit the fitness and diversity information of the population, this paper presents a DE framework with multiobjective sorting-based mutation operator. In the proposed mutation operator, individuals in the current population are firstly sorted according to their fitness and diversity contribution by nondominated sorting. Then parents in the mutation operators are proportionally selected according to their rankings based on fitness and diversity, thus, the promising individuals with better fitness and diversity have more opportunity to be selected as parents. Since fitness and diversity information is simultaneously considered for parent selection, a good balance between exploration and exploitation can be achieved. The proposed operator is applied to original DE algorithms, as well as several advanced DE variants. Experimental results on 48 benchmark functions and 12 real-world application problems show that the proposed operator is an effective approach to enhance the performance of most DE algorithms studied.

Differential evolution with neighborhood and direction information for numerical optimization.

Differential evolution (DE) is a simple and powerful population-based evolutionary algorithm, successfully used in various scientific and engineering fields. Although DE has been studied by many researchers, the neighborhood and direction information is not fully and simultaneously exploited in the designing of DE. In order to alleviate this drawback and enhance the performance of DE, we first introduce two novel operators, namely, the neighbor guided selection scheme for parents involved in mutation and the direction induced mutation strategy, to fully exploit the neighborhood and direction information of the population, respectively. By synergizing these two operators, a simple and effective DE framework, which is referred to as the neighborhood and direction information based DE (NDi-DE), is then proposed for enhancing the performance of DE. This way, NDi-DE not only utilizes the information of neighboring individuals to exploit the regions of minima and accelerate convergence but also incorporates the direction information to prevent an individual from entering an undesired region and move to a promising area. Consequently, a good balance between exploration and exploitation can be achieved. In order to test the effectiveness of NDi-DE, the proposed framework is applied to the original DE algorithms, as well as several state-of-the-art DE variants. Experimental results show that NDi-DE is an effective framework to enhance the performance of most of the DE algorithms studied.